Identifying potential biomarkers for early evaluating mechanical compression injuries to skeletal muscle through proteomic analysis: A rat model.

Skeletal muscle is the most abundant tissue in the body, and it accounts for 40%-50% of total body mass in healthy individuals, playing a crucial role in maintaining quality of life [ 1 ]. However, it is also susceptible to damages in daily production and life, and its severity of injuries varies [ 2 ]. Skeletal muscles have a certain degree of self-healing ability after injury. However, when the damage exceeds their regenerative capacity, complete recovery cannot be achieved. Severe skeletal muscle injuries often result in incomplete regeneration, leading to functional impairments that significantly impact patients’ daily work and life abilities. In clinical practice, understanding the degree of skeletal muscle injury in patients with limb trauma is crucial for selecting treatment modalities and assessing prognosis. Currently, there is still a lack of systematic evaluation methods and indicators for assessing the degree of skeletal muscle injury. Therefore, understanding the molecular characteristics of skeletal muscle injury at different degrees in peripheral blood can provide strong support for the development of early diagnostic biomarkers for assessment on skeletal muscle injury, and for the further development of targeted clinical therapies for skeletal muscle repair. Before establishing systematic screening procedures for the genome, transcriptome, proteome, and metabolome, targeted biomedical investigations are a common approach to identify disease biomarkers [ 3 ]. The existing classical serum protein markers related to muscle tissue breakdown and fiber injury include creatine kinase, lactate dehydrogenase, myoglobin, aldolase, and aspartate aminotransferase, among which creatine kinase is the most commonly used serum marker for muscle injury [ 4 ]. However, these established enzymes lack high diagnostic reliability, as creatine kinase can still increase after regular exercise [ 5 ]. They also cannot effectively differentiate the severity of the injury. With the rapid development of life science technologies, especially high-throughput techniques, the detection methods for biomarkers after injury have been greatly improved [ 6 – 9 ]. Liu et al. [ 7 ] investigated the changes in the rat muscle proteome during the phases of damage, repair, and early remodeling following impact injury. Li et al. [ 9 ] applied gene chips and bioinformatics analysis to describe the molecular spectrum changes after skeletal muscle contusion. However, there have been no reports on the characteristics of early plasma proteomics after varying degrees of mechanical compression injury in rat skeletal muscle. Therefore, this study aims to establish a rat model of mechanical compression injury in skeletal muscle. Plasma protein expression profiles of different injury groups were detected using LC-MS/MS-4D-DIA quantitative proteomics technique. Bioinformatic analysis was performed to decode the data, aiming to explore specific biomarkers suitable for early diagnosis of skeletal muscle injuries in varying degrees. These biomarkers could assist clinicians in evaluating the levels of skeletal muscle injury for more personalized treatment therapies, thereby improving patient outcomes and quality of life. This study could also provide solid scientific foundation for further exploration of the underlying mechanisms of the injury, repair and regeneration of skeletal muscle.

The morphological characteristics of skeletal muscle injury in rats from each group were observed 3 days post-injury using H&E staining. As shown in Fig 3A , H&E staining revealed mild disruption of muscle fibers and moderate interstitial edema accompanied by inflammatory cell infiltration in the mild injury group. In the severely injured group, muscle fibers exhibited severe disruption accompanied by a significant infiltration of inflammatory cells. The proportion of injured fibers in the severe injury group was significantly higher than that in the mild injury group and the control group ( Fig 3B ). At 7 and 14 days post-injury, regenerated muscle fibers with central nuclei were observed in all injury groups. However, the number and diameter of regenerated muscle fibers in the mild injury group were both greater than those in the severe injury group ( Fig 3C – 3H ). At 28 days post-injury, although some muscle fibers with central nuclei could still be observed in the mild injury group, the average muscle fiber area and histopathological characteristics showed no difference compared to the control group. In contrast, the average muscle fiber area in the severe injury group was significantly smaller than that of the control group, with extensive fibrosis observed. ( Fig 4 ). (A) Typical H&E staining images of tibialis anterior muscle 3 days post-injury. (B) Percentage of injured myofibers at 3 days post-injury. (C) Representative images of muscle regeneration at 7 days post-injury. (D) Quantitative analysis of the number of regenerated muscle fibers in each group at 7 days post-injury. (E) Representative images of muscle regeneration at 14 days post-injury. (F) Quantitative analysis of the number of regenerated muscle fibers in each group at 14 days post-injury. (G) Quantitative analysis of the diameter of regenerated muscle fibers in each group at 7 days post-injury. (H) Quantitative analysis of the diameter of regenerated muscle fibers in each group at 14 days post-injury. All data are presented as mean ± standard deviation. N = 5. (A) Representative images of muscle regeneration, as shown by H&E staining. (B) Quantitative analysis of the average myofiber size in each group. (C) Representative image of fibrosis in each group, as shown by Masson trichrome staining. (D) Quantitative analysis of the fibrotic area in each group. (E) Representative image of Sirius Red staining at 28 days post-injury under bright field and polarized light microscopy. (F) Quantitative analysis of collagen fiber area under bright field microscopy. (G) Quantitative analysis of type I and type III collagen fiber area under polarized light. All data are presented as mean ± standard deviation. N = 5. A total of 2,797 proteins with at least one unique peptide were identified with an FDR ≤ 1% ( Fig 5A ). Through differential analysis, when P value was less than 0.05, fold change exceeding 1.5 was considered as significant upregulation threshold, and being less than 1/1.5 was considered as significant downregulation threshold. Compared to the C group, the M group exhibited significant differences in 237 proteins (198 upregulated, 39 downregulated), while the S group showed significant differences in 541 proteins (412 upregulated, 129 downregulated). Relative to the M group, the S group displayed significant differences in 287 proteins (204 upregulated, 83 downregulated) ( Fig 5B ). The volcano plot illustrated significant changes in protein expression levels among pairwise comparisons of the three groups. Red dots represented significant upregulation, blue dots represented significant downregulation, and gray dots indicated no significant difference. The top 5 upregulated and downregulated proteins (sorted by absolute Log2 Ratio values) were concurrently labeled in the plot ( Fig 5C – 5E ). (A) The total number of peptides and proteins identified. (B) Bar graph showed the number of differentially expressed proteins in pairwise comparisons among the three groups. (C-E) Volcano plots of pairwise comparisons among the three groups. (C) Comparison between the mild injury group and the control group. (D) Comparison between the severe injury group and the control group. (E) Comparison between the severe injury group and the mild injury group. Red dots represented significantly upregulated proteins, blue dots represented significantly downregulated proteins, and gray dots represented proteins with no significant difference. The names of the top 5 upregulated and downregulated proteins (ranked by absolute Log2 Ratio) were labeled in the plot. Intersection protein analysis was performed to identify proteins with significant differential expression capable of distinguishing various degrees of muscle damage. The relative quantification values of 37 proteins increased with the severity of muscle damage, while those of 2 proteins decreased as the degree of muscle damage escalated ( Fig 6A ). The heatmap depicted the relative expression levels of 39 proteins across the three groups and illustrated their clustering relationships. Each row represented a differentially expressed protein, while each column represented a sample. Red indicated high expression, blue indicated low expression, and gray indicated non-quantifiable levels in the corresponding sample ( Fig 6B ). The detailed information on differentially expressed proteins (DEPs) along with fold changes is listed in Table 1 . (A) The union of the intersection between the differentially upregulated proteins in pairwise comparisons MvsC and SvsM, and the intersection between the differentially downregulated proteins in pairwise comparisons MvsC and SvsM, yielded a total of 39 proteins. (B) a quantitative heatmap of the intersecting proteins. GO and KEGG enrichment analysis was performed on the 39 proteins that could be used to distinguish different degrees of muscle injury. Gene Ontology (GO) enrichment analysis outlined the functions of the aforementioned DEPs. In terms of cellular components, DEPs primarily involved contractile fibers, myofibrils, sarcomeres, I-bands, and Z-discs ( Fig 7A ). DEPs mainly pertained to gluconeogenesis, NAD metabolism, pyruvate metabolism, nicotinamide nucleotide metabolic process and nucleotide biosynthesis biological processes ( Fig 7B ). Molecularly, DEPs were primarily associated with binding to heterocyclic compounds, small molecules, nucleotides, and NAD ( Fig 7C ). (A) Cellular component of DEPs. (B) Biological processes of DEPs. (C) Molecular function of DEPs. (D) KEGG pathways. The x-axis represented the fold enrichment of functional enrichment after Log2 transformation, with larger values indicating higher enrichment levels. The color of the dots indicated the significance of enrichment P value, with darker blue indicating stronger enrichment significance. The size of the dots represented the number of differential proteins, with larger dots indicating a greater number of differential proteins. Based on KEGG-based functional enrichment analysis, DEPs were significantly enriched in multiple processes ( Fig 7D ), including glycolysis/gluconeogenesis, pyruvate metabolism, cysteine and methionine metabolism, as well as starch and sucrose metabolism. The GO and KEGG analysis of DEPs indicated that some skeletal muscle-specific structural proteins and metabolism-related proteins were significantly upregulated. These proteins might leak into the blood as a result of skeletal muscle rupture, including Fast skeletal muscle Troponin T, slow skeletal type Troponin I1, Four and a half LIM domains 1, PDZ and LIM domain 3, Hormone-sensitive lipase, Alpha-1,4 glucan phosphorylase, cytoplasmic glycerol-3-phosphate dehydrogenase, Phosphoglucomutase 1, Nicotinamide phosphoribosyltransferase, and Glucose-6-phosphate isomerase. In general, proteins with high degree or MCC are more likely to be key proteins. Glucose-6-phosphate isomerase (Gpi) exhibited the highest degree and MCC value. The functions of other proteins in the PPI network were primarily involved in carbohydrate metabolism ( Fig 8 ). These data also indicated dramatic changes in metabolic-related processes during muscle injury. The significantly altered proteins may serve as biomarkers for assessing muscle injury. The color represented the MCC value of DEPs, with darker colors indicating higher MCC values. The size of the node represented the degree, where larger nodes indicated higher degrees. Generally, proteins with higher degrees or MCC values were more likely to be key proteins.

We induced varying degrees of mechanical compression injury in the skeletal muscles of the rat’s lower leg using a novel compression device. Rats in the mild and severe injury groups exhibited completely different degrees of injury and prognosis. We conducted LC-MS/MS-4D-DIA quantitative proteomics analysis for the first time on the plasma of rats in the control, mild injury, and severe injury group. The results showed significant differences in fsTnT, ssTnI, Fhl1, Pdlim3, HSL, Pygm, Gpd1, Pgm1, Nampt, and Gpi among the three groups. These proteins may serve as novel biomarkers for the early evaluation different degrees of muscle injury in the future.

All experiments received approval from the Animal Care and Use Committee of the Third Hospital of Hebei Medical University (Z2022-009-1) and were conducted following the National Institutes of Health Guidelines for the Care and Use of Laboratory Animals. Fifty-five Sprague-Dawley (SD) rats (male, 10–11 weeks old, weighing between 350-360g) were procured from Beijing Huafukang Biotechnology Co., Ltd. The rats were housed under controlled conditions, including standard chow, 12-hour light/12-hour dark cycles, and maintained at a constant temperature (22–24 °C) and humidity (50–65%). Experiments were consistently conducted at similar times to mitigate any potential circadian rhythm effects on the rats. A novel compression device, consisting of a pressure gauge cuff embedded in a rigid plastic tube as described earlier [ 10 ], was used to induce varying degrees of mechanical compression injury in the lower leg skeletal muscle of the rat. This simulated crush injuries of varying degrees in the limbs of clinical patients. This device cuff could fully envelop the entire lower leg of the rat. After a 7-day acclimatization period, fifty-five rats were randomly divided into three groups, namely the control group (C), the mild injury group (M), and the severe injury group (S). Control group consisted of 5 rats that were only anesthetized and fixed in the compression device without pressure. The Mild injury group included 25 rats subjected to a pressure of 300 mmHg for 2 hours. Similarly, the Severe injury group comprised 25 rats exposed to a pressure of 300 mmHg for 6 hours ( Fig 1 ). In the injury group, 25 rats were euthanized at five time points after injury: 3 hours, 3 days, 7 days, 14 days, and 28 days, with 5 rats at each time point. All tested samples were collected accordingly. SD rats were anesthetized using 3% isoflurane for induction, followed by maintenance with 1.5% isoflurane. Once fully anesthetized, the right lower limb was shaved for modeling purposes. Postoperative analgesia was administered with 1% lidocaine infiltration and continued until sampling time. After the injury, the rats were transferred to clean cages with unrestricted access to food and water. At the conclusion of the experiment, all animals were euthanized by intraperitoneal injection of 200 mg/kg sodium pentobarbital and 10mg/mL lidocaine. Subsequently, all animal carcasses were uniformly incinerated. The experimental procedure was shown in Fig 2 . In order to obtain blood biomarkers that could be used to assess varying degrees of muscle injury in the early stages of injury, blood samples were collected by cardiac puncture from three groups of rats (n = 5) 3 hours post-injury. After centrifugation (10 minutes, 3000rpm), the plasma samples were rapidly frozen in liquid nitrogen and stored in a -80°C freezer for subsequent proteomic analysis. At this point, tibialis anterior (TA) muscles from the control group rats were collected as a normal control group. At 3d, 7d, 14d, and 28d, muscle samples of the injured side tibialis anterior (TA) from the injury group were collected for histological examination. The muscles were fixed in 10% neutral buffered formalin (P110, Solarbio, China) for 48 hours and then transferred to 70% ethanol (Yongda Chemical Reagent Company, China). The TA muscles were embedded in paraffin and sectioned into 4 μm thick slices. Muscle sections obtained at 3 days, 7 days, 14 days, and 28 days post-injury were stained with Hematoxylin and Eosin (H&E) to assess the extent of muscle injury and regeneration. Muscle sections obtained at 28 days post-injury were stained with Masson trichrome and Sirius Red (Servicebio, Wuhan, China) to assess the degree of fibrosis. Stained sections were analyzed at 200x magnification using a Nikon Eclipse Ci-L microscope (Japan). The determination of the proportion of injured muscle fibers was conducted according to the scheme outlined by McCormack et al [ 11 ]. Injured myofibers are defined as ragged cellular edges, vacuolation, Inflammatory cell infiltration, or rhabdomyolysis [ 12 ]. Regenerative myofibers were identified as those containing central nuclei [ 13 ]. At 7 days and 14 days post-injury, five random fields were selected from each sample at 20x objective lens magnification to quantify the total number of regenerating myofibers, and the short-axis diameter of each regenerating myofiber was measured, as described previously [ 14 ]. At 28 days post-injury, five random fields were selected from each sample at 20x objective lens magnification, and the average cross-sectional area of myofibers in each group was quantified using Image-Pro Plus 6.0 software [ 15 ]. At 28 days post-injury, five randomly selected regions from each sample stained with Masson trichrome and Sirius Red at 20x objective lens magnification were quantitatively measured for fibrosis using Image-Pro Plus 6.0 software. Initially, cellular debris was eliminated from the plasma sample by centrifugation at 12,000 g at 4 °C for 10 minutes. Subsequently, the supernatant was transferred to a new centrifuge tube. Finally, the protein concentration was assessed using a BCA kit (Thermo Fisher Scientific, USA) following the manufacturer’s guidelines. 50 µL of centrifuged high-speed blood sample were transferred to pre-washed magnetic nanoparticles (PTM-00F13303, sourced from PTM Bio, Hangzhou Jingjie Biotechnology Co., Ltd.), and incubated at 37°C for 1 hour on a constant temperature mixer at 1200 rpm. After the incubation, the magnetic beads were washed three times with washing buffer. Subsequently, 70 μL of enzymatic digestion buffer was added to the beads. After mixing, they were heated at 95°C for 10 minutes. Following the heating process, the samples were allowed to return to room temperature. Then, trypsin was added to a final concentration of 20 ng/μL, and the mixture was incubated at 37°C overnight for enzymatic digestion. Dithiothreitol (DTT) was added to achieve a final concentration of 5 mM, and the mixture was reduced at 56°C for 30 minutes. Subsequently, iodoacetamide (IAM) was added to achieve a final concentration of 11 mM, and the mixture was incubated at room temperature in the dark for 15 minutes. Finally, desalination was performed according to the C18 ZipTips (MilliporeSigma, Germany) manual, followed by vacuum freeze-drying for subsequent liquid chromatography tandem-mass spectrometry (LC-MS/ MS) analysis. The mobile phase consisted of solvent A (0.1% formic acid, 2% acetonitrile in water) and solvent B (0.1% formic acid, 90% acetonitrile in water). The tryptic peptides were dissolved in solvent A and directly injected onto a homemade reversed-phase analytical column (25 cm length, 100 μm i.d.). Peptides were eluted with the following gradient: 0–16 min, 6%-20% B; 16–24 min, 20%-32% B; 24–27 min, 32%-80% B; 27–30 min, 80% B, all at a constant flow rate of 500 nl/min using an EASY-nLC 1200 UPLC system (Thermo Fisher Scientific, USA). The separated peptides were analyzed in an Orbitrap Exploris 480 mass spectrometer equipped with a nano-electrospray ion source (Thermo Fisher Scientific, USA). The electrospray voltage applied was 2,100 V. Precursor ions and fragments were detected in the Orbitrap detector. The full MS scan resolution was set to 30,000 over a scan range of 350–1,050 m/z. The MS/MS scan was triggered by precursor ions with a minimum m/z of 200.0 and a resolution of 450,000. HCD fragmentation was carried out at normalized collision energies (NCE) of 25%, 30%, and 35%. The automatic gain control (AGC) target was set to 3E6, with a maximum injection time set to Auto. The DIA data were analyzed using the DIA-NN search engine (v.1.8). Tandem mass spectra were searched against Rattus_norvegicus_10116_PR_20231121.fasta (47943 entries) concatenated with reverse decoy database. Trypsin/P was designated as the cleavage enzyme with allowance for up to 1 missing cleavage. Fixed modifications included excision on N-terminal Met and carbamidomethyl on Cys. False Discovery Rate (FDR) was adjusted to < 1%. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed on differentially expressed proteins. Gene Ontology analysis is a bioinformatic analysis method that organically links various information about genes and gene products (such as proteins) to provide statistical information [ 16 ]. The GO annotation process entailed extracting GO IDs from identified proteins using eggnog-mapper software based on the EggNOG database. Subsequently, functional classification annotation analysis was conducted on the proteins based on cellular components, molecular functions, and biological processes. For each category, a two-tailed Fisher’s exact test was used to test the enrichment of differentially expressed proteins relative to all identified proteins. A corrected p-value <0.05 was considered significant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) integrates the currently known protein-protein interaction network information. KEGG pathways mainly include metabolism, genetic information processing, environmental information processing, cellular processes, human diseases, and drug development. We annotated protein pathways based on the KEGG pathway database, and performed BLAST comparisons (blastp, evalue ≤ 1e-4) for the identified proteins. For each sequence, the annotation was based on the top-scoring comparison result. We conducted pathway enrichment analysis for differentially expressed proteins using Fisher’s exact test (Fisher’s exact test), with the identified proteins as the background. A p-value  0.7). A visual network was constructed using Cytoscape 3.10.0, and the top ten hub genes were identified using the cytoHubba plugin (MCC algorithm). Python (version 3.7.6) was used to perform the statistical analyses. A one-way ANOVA was performed to check for significant differences between the groups. After finding significance, Tukey’s HSD test was used for post-hoc analysis, with p < 0.05 indicating statistical significance.

(XLSX) (TIF) (A) Cellular component of DEPs. (B) Biological processes of DEPs. (C) Molecular function of DEPs. (D) KEGG pathways. (TIF) (TIF)